11 papers
Uncertainty Quantification for Computer-Use Agents: A Benchmark across Vision-Language Models and GUI Grounding Datasets
Divake Kumar, Sina Tayebati, Devashri Naik +5
Computer-use agents turn vision-language model (VLM) predictions into executable GUI clicks, so reliable uncertainty estimates are essential for rejection, calibration, miss-severi…
TRIAGE: Type-Routed Interventions via Aleatoric-Epistemic Gated Estimation in Robotic Manipulation and Adaptive Perception -- Don't Treat All Uncertainty the Same
Divake Kumar, Sina Tayebati, Devashri Naik +4
Most uncertainty-aware robotic systems collapse prediction uncertainty into a single scalar score and use it to trigger uniform corrective responses. This aggregation obscures whet…
Parts-Mamba: Augmenting Joint Context with Part-Level Scanning for Occluded Human Skeleton
Tianyi Shen, Huijuan Xu, Nilesh Ahuja +3
Skeleton action recognition involves recognizing human action from human skeletons. The use of graph convolutional networks (GCNs) has driven major advances in this recognition tas…
Calibrated Decomposition of Aleatoric and Epistemic Uncertainty in Deep Features for Inference-Time Adaptation
Divake Kumar, Patrick Poggi, Sina Tayebati +3
Most estimators collapse all uncertainty modes into a single confidence score, preventing reliable reasoning about when to allocate more compute or adjust inference. We introduce U…
MoRA: Missing Modality Low-Rank Adaptation for Visual Recognition
Shu Zhao, Nilesh Ahuja, Tan Yu +2
Pre-trained vision language models have shown remarkable performance on visual recognition tasks, but they typically assume the availability of complete multimodal inputs during bo…
Windsock is Dancing: Adaptive Multimodal Retrieval-Augmented Generation
Shu Zhao, Tianyi Shen, Nilesh Ahuja +2
Multimodal Retrieval-Augmented Generation (MRAG) has emerged as a promising method to generate factual and up-to-date responses of Multimodal Large Language Models (MLLMs) by incor…